Type 2 diabetes is a heterogeneous disease, whose pathophysiology results from various disruptions in glucose metabolism. According to some experts, current diagnostic methods do not provide sufficient information about individual metabolic deviations. A research team led by Ahmed A. Metwally from Stanford University, in a study published in the journal Nature Biomedical Engineering, proposes a method that identifies metabolic subphenotypes of type 2 diabetes using continuous glucose monitoring and machine learning algorithms. This new method…
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